Prevalence and Predictors of Psychological Violence Against Male Victims in Intimate Relationships in Canada
Bibliographic record
Abstract
Psychological violence involves expressive violence (i.e., the use of words to humiliate or psychologically harm a partner) and coercive violence (i.e., controlling behavior directed to dominate and manipulate a partner). From studies that collect data on both physical and psychological violence, it is apparent that psychological violence is the most prevalent form of intimate partner violence (IPV). However, psychological violence is one of the dimensions of IPV that has received relatively less attention. Furthermore, very little is known about the state of female-to-male psychological violence, as most studies on the subject have focused on female victims. This study seeks to understand recent trends and prevalence of psychological violence in male-to-female and female-to-male relationships in Canada. Using the 2014 General Social Survey (Victimization) data, the risk factors of female-to-male psychological violence were analyzed. The findings of the analysis revealed that there are significant differences in the prevalence of psychological violence among victims when gender is taken into consideration. The study also revealed that childhood victimization, childhood exposure to domestic violence, marijuana use, and educational attainment are predictors of female-to-male psychological violence. The study highlights the need for more studies to be conducted to attain a better understanding of the nature and context of psychological violence in heterosexual relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".